LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data
LSH-GAN is a locality-sensitive hashing based generative adversarial model that can produce realistic cell samples from small sample single-cell scRNA-seq data. The generated cells can be utilized for downstream analysis, like gene selection and cell clustering.
Gardado en:
| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
Nature Portfolio
2022-06-01
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| Series: | Communications Biology |
| Acceso en liña: | https://doi.org/10.1038/s42003-022-03473-y |
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